"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .byobnet import ByoBlockCfg, ByoModelCfg, ByobNet, interleave_blocks
from .helpers import build_model_with_cfg
from .registry import register_model

__all__ = []


def _cfg(url='', **kwargs):
    return {
        'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bicubic',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'stem.conv1.conv', 'classifier': 'head.fc',
        'fixed_input_size': False, 'min_input_size': (3, 224, 224),
        **kwargs
    }


default_cfgs = {
    # GPU-Efficient (ResNet) weights
    'botnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
    'botnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
    'eca_botnext26ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),

    'halonet_h1': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
    'halonet_h1_c4c5': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
    'halonet26t': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
    'halonet50ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),
    'eca_halonext26ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8), min_input_size=(3, 256, 256)),

    'lambda_resnet26t': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),
    'lambda_resnet50t': _cfg(url='', min_input_size=(3, 128, 128)),
    'eca_lambda_resnext26ts': _cfg(url='', min_input_size=(3, 128, 128), input_size=(3, 256, 256), pool_size=(8, 8)),

    'swinnet26t_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
    'swinnet50ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),
    'eca_swinnext26ts_256': _cfg(url='', fixed_input_size=True, input_size=(3, 256, 256), pool_size=(8, 8)),

    'rednet26t': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
    'rednet50ts': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)),
}


model_cfgs = dict(

    botnet26t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        fixed_input_size=True,
        self_attn_layer='bottleneck',
        self_attn_kwargs=dict()
    ),
    botnet50ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=6, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=1, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='',
        num_features=0,
        fixed_input_size=True,
        act_layer='silu',
        self_attn_layer='bottleneck',
        self_attn_kwargs=dict()
    ),
    eca_botnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=16, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=16, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=16, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        fixed_input_size=True,
        act_layer='silu',
        attn_layer='eca',
        self_attn_layer='bottleneck',
        self_attn_kwargs=dict()
    ),

    halonet_h1=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='self_attn', d=3, c=64, s=1, gs=0, br=1.0),
            ByoBlockCfg(type='self_attn', d=3, c=128, s=2, gs=0, br=1.0),
            ByoBlockCfg(type='self_attn', d=10, c=256, s=2, gs=0, br=1.0),
            ByoBlockCfg(type='self_attn', d=3, c=512, s=2, gs=0, br=1.0),
        ),
        stem_chs=64,
        stem_type='7x7',
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='halo',
        self_attn_kwargs=dict(block_size=8, halo_size=3),
    ),
    halonet_h1_c4c5=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=64, s=1, gs=0, br=1.0),
            ByoBlockCfg(type='bottle', d=3, c=128, s=2, gs=0, br=1.0),
            ByoBlockCfg(type='self_attn', d=10, c=256, s=2, gs=0, br=1.0),
            ByoBlockCfg(type='self_attn', d=3, c=512, s=2, gs=0, br=1.0),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='halo',
        self_attn_kwargs=dict(block_size=8, halo_size=3),
    ),
    halonet26t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='halo',
        self_attn_kwargs=dict(block_size=8, halo_size=2)  # intended for 256x256 res
    ),
    halonet50ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=6, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        self_attn_layer='halo',
        self_attn_kwargs=dict(block_size=8, halo_size=2)
    ),
    eca_halonext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        attn_layer='eca',
        self_attn_layer='halo',
        self_attn_kwargs=dict(block_size=8, halo_size=2)  # intended for 256x256 res
    ),

    lambda_resnet26t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='lambda',
        self_attn_kwargs=dict()
    ),
    lambda_resnet50t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=3, d=6, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='lambda',
        self_attn_kwargs=dict()
    ),
    eca_lambda_resnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
            ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=16, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        attn_layer='eca',
        self_attn_layer='lambda',
        self_attn_kwargs=dict()
    ),

    swinnet26t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        fixed_input_size=True,
        self_attn_layer='swin',
        self_attn_kwargs=dict(win_size=8)
    ),
    swinnet50ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=4, c=512, s=2, gs=0, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        fixed_input_size=True,
        act_layer='silu',
        self_attn_layer='swin',
        self_attn_kwargs=dict(win_size=8)
    ),
    eca_swinnext26ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=16, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=512, s=2, gs=16, br=0.25),
            interleave_blocks(types=('bottle', 'self_attn'), every=1, d=2, c=1024, s=2, gs=16, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=16, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        fixed_input_size=True,
        act_layer='silu',
        attn_layer='eca',
        self_attn_layer='swin',
        self_attn_kwargs=dict(win_size=8)
    ),


    rednet26t=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='self_attn', d=2, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=512, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',  # FIXME RedNet uses involution in middle of stem
        stem_pool='maxpool',
        num_features=0,
        self_attn_layer='involution',
        self_attn_kwargs=dict()
    ),
    rednet50ts=ByoModelCfg(
        blocks=(
            ByoBlockCfg(type='self_attn', d=3, c=256, s=1, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=4, c=512, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=2, c=1024, s=2, gs=0, br=0.25),
            ByoBlockCfg(type='self_attn', d=3, c=2048, s=2, gs=0, br=0.25),
        ),
        stem_chs=64,
        stem_type='tiered',
        stem_pool='maxpool',
        num_features=0,
        act_layer='silu',
        self_attn_layer='involution',
        self_attn_kwargs=dict()
    ),
)


def _create_byoanet(variant, cfg_variant=None, pretrained=False, **kwargs):
    return build_model_with_cfg(
        ByobNet, variant, pretrained,
        default_cfg=default_cfgs[variant],
        model_cfg=model_cfgs[variant] if not cfg_variant else model_cfgs[cfg_variant],
        feature_cfg=dict(flatten_sequential=True),
        **kwargs)


@register_model
def botnet26t_256(pretrained=False, **kwargs):
    """ Bottleneck Transformer w/ ResNet26-T backbone. Bottleneck attn in final stage.
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('botnet26t_256', 'botnet26t', pretrained=pretrained, **kwargs)


@register_model
def botnet50ts_256(pretrained=False, **kwargs):
    """ Bottleneck Transformer w/ ResNet50-T backbone. Bottleneck attn in final stage.
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('botnet50ts_256', 'botnet50ts', pretrained=pretrained, **kwargs)


@register_model
def eca_botnext26ts_256(pretrained=False, **kwargs):
    """ Bottleneck Transformer w/ ResNet26-T backbone. Bottleneck attn in final stage.
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('eca_botnext26ts_256', 'eca_botnext26ts', pretrained=pretrained, **kwargs)


@register_model
def halonet_h1(pretrained=False, **kwargs):
    """ HaloNet-H1. Halo attention in all stages as per the paper.

    This runs very slowly, param count lower than paper --> something is wrong.
    """
    return _create_byoanet('halonet_h1', pretrained=pretrained, **kwargs)


@register_model
def halonet_h1_c4c5(pretrained=False, **kwargs):
    """ HaloNet-H1 config w/ attention in last two stages.
    """
    return _create_byoanet('halonet_h1_c4c5', pretrained=pretrained, **kwargs)


@register_model
def halonet26t(pretrained=False, **kwargs):
    """ HaloNet w/ a ResNet26-t backbone, Hallo attention in final stage
    """
    return _create_byoanet('halonet26t', pretrained=pretrained, **kwargs)


@register_model
def halonet50ts(pretrained=False, **kwargs):
    """ HaloNet w/ a ResNet50-t backbone, Hallo attention in final stage
    """
    return _create_byoanet('halonet50ts', pretrained=pretrained, **kwargs)


@register_model
def eca_halonext26ts(pretrained=False, **kwargs):
    """ HaloNet w/ a ResNet26-t backbone, Hallo attention in final stage
    """
    return _create_byoanet('eca_halonext26ts', pretrained=pretrained, **kwargs)


@register_model
def lambda_resnet26t(pretrained=False, **kwargs):
    """ Lambda-ResNet-26T. Lambda layers in one C4 stage and all C5.
    """
    return _create_byoanet('lambda_resnet26t', pretrained=pretrained, **kwargs)


@register_model
def lambda_resnet50t(pretrained=False, **kwargs):
    """ Lambda-ResNet-50T. Lambda layers in one C4 stage and all C5.
    """
    return _create_byoanet('lambda_resnet50t', pretrained=pretrained, **kwargs)


@register_model
def eca_lambda_resnext26ts(pretrained=False, **kwargs):
    """ Lambda-ResNet-26T. Lambda layers in one C4 stage and all C5.
    """
    return _create_byoanet('eca_lambda_resnext26ts', pretrained=pretrained, **kwargs)


@register_model
def swinnet26t_256(pretrained=False, **kwargs):
    """
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('swinnet26t_256', 'swinnet26t', pretrained=pretrained, **kwargs)


@register_model
def swinnet50ts_256(pretrained=False, **kwargs):
    """
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('swinnet50ts_256', 'swinnet50ts', pretrained=pretrained, **kwargs)


@register_model
def eca_swinnext26ts_256(pretrained=False, **kwargs):
    """
    """
    kwargs.setdefault('img_size', 256)
    return _create_byoanet('eca_swinnext26ts_256', 'eca_swinnext26ts', pretrained=pretrained, **kwargs)


@register_model
def rednet26t(pretrained=False, **kwargs):
    """
    """
    return _create_byoanet('rednet26t', pretrained=pretrained, **kwargs)


@register_model
def rednet50ts(pretrained=False, **kwargs):
    """
    """
    return _create_byoanet('rednet50ts', pretrained=pretrained, **kwargs)
